Q-LFR METHOD FOR MATCHING MINUTIAE FINGERPRINT IDENTICAL FEATURES
Veerendra Kumar Pathak, Nikhil Pateriya
PG STUDENT, JNCT
Because of its non-invasiveness, high precision recognition and the use of fingerprints are one of the most consistent biometric symbols in the context of human recognition and identification. In this paper here they suggested that a new method of machine learning can be used to find minutiae on low-resolution finger images. Traditional methods use the first step of preparation but due to the lack of intensity to be very sensitive to sound and image quality. We suggest a solid path where fingerprints are found to see the minutiae. Here they use machine learning to improve image quality and the most beneficial policy. Multi-layer ideas with in-depth learning strategies are used for a large area of the state and then select the appropriate reward structure and study area to learn the distribution. One of the major problems is that minutiae development facilities are easily accessible and their learning activity. The test result shows that our algorithm provides the best results in both parameters.
Keywords: Fingerprint, Minutiae extraction, Convolution Neutral Network, Support Vector Machine, Principal Component Analysis
Journal Name :
EPRA International Journal of Multidisciplinary Research (IJMR)
Published on : 2021-08-25